Data
Yeast-train

Yeast-train

in_preparation ARFF Publicly available Visibility: public Uploaded 20-06-2017 by Stefan Coors
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Source: Creator and Maintainer: Kenta Nakai Institue of Molecular and Cellular Biology Osaka, University 1-3 Yamada-oka, Suita 565 Japan nakai '@' imcb.osaka-u.ac.jp http://www.imcb.osaka-u.ac.jp/nakai/psort.html Donor: Paul Horton (paulh '@' cs.berkeley.edu) Data Set Information: Predicted Attribute: Localization site of protein. ( non-numeric ). The references below describe a predecessor to this dataset and its development. They also give results (not cross-validated) for classification by a rule-based expert system with that version of the dataset. Reference: "Expert Sytem for Predicting Protein Localization Sites in Gram-Negative Bacteria", Kenta Nakai & Minoru Kanehisa, PROTEINS: Structure, Function, and Genetics 11:95-110, 1991. Reference: "A Knowledge Base for Predicting Protein Localization Sites in Eukaryotic Cells", Kenta Nakai & Minoru Kanehisa, Genomics 14:897-911, 1992. Attribute Information: 1. Sequence Name: Accession number for the SWISS-PROT database 2. mcg: McGeoch's method for signal sequence recognition. 3. gvh: von Heijne's method for signal sequence recognition. 4. alm: Score of the ALOM membrane spanning region prediction program. 5. mit: Score of discriminant analysis of the amino acid content of the N-terminal region (20 residues long) of mitochondrial and non-mitochondrial proteins. 6. erl: Presence of "HDEL" substring (thought to act as a signal for retention in the endoplasmic reticulum lumen). Binary attribute. 7. pox: Peroxisomal targeting signal in the C-terminus. 8. vac: Score of discriminant analysis of the amino acid content of vacuolar and extracellular proteins. 9. nuc: Score of discriminant analysis of nuclear localization signals of nuclear and non-nuclear proteins. #autoxgboost #autoweka

9 features

f0numeric77 unique values
0 missing
f1numeric76 unique values
0 missing
f2numeric51 unique values
0 missing
f3numeric71 unique values
0 missing
f4numeric2 unique values
0 missing
f5numeric3 unique values
0 missing
f6numeric45 unique values
0 missing
f7numeric66 unique values
0 missing
classnominal10 unique values
0 missing

62 properties

1039
Number of instances (rows) of the dataset.
9
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
8
Number of numeric attributes.
1
Number of nominal attributes.
-0.09
First quartile of skewness among attributes of the numeric type.
0.38
Mean of means among attributes of the numeric type.
0.06
First quartile of standard deviation of attributes of the numeric type.
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
4.59
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.01
Number of attributes divided by the number of instances.
10
Average number of distinct values among the attributes of the nominal type.
0.5
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
3.01
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Percentage of instances belonging to the most frequent class.
0.1
Mean standard deviation of attributes of the numeric type.
0.95
Second quartile (Median) of skewness among attributes of the numeric type.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0
Percentage of binary attributes.
0.1
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
0.27
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
153.12
Maximum kurtosis among attributes of the numeric type.
0.01
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
64.05
Third quartile of kurtosis among attributes of the numeric type.
0.51
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
88.89
Percentage of numeric attributes.
0.5
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
10
The minimal number of distinct values among attributes of the nominal type.
11.11
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
10
The maximum number of distinct values among attributes of the nominal type.
-1.82
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
7.44
Third quartile of skewness among attributes of the numeric type.
12.39
Maximum skewness among attributes of the numeric type.
0.05
Minimum standard deviation of attributes of the numeric type.
0.81
First quartile of kurtosis among attributes of the numeric type.
0.13
Third quartile of standard deviation of attributes of the numeric type.
0.14
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0.26
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
32.1
Mean kurtosis among attributes of the numeric type.

16 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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